Overview

Brought to you by YData

Dataset statistics

Number of variables19
Number of observations170653
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory24.7 MiB
Average record size in memory152.0 B

Variable types

Numeric13
Text3
Categorical2
DateTime1

Alerts

acousticness is highly overall correlated with energy and 3 other fieldsHigh correlation
danceability is highly overall correlated with valenceHigh correlation
energy is highly overall correlated with acousticness and 2 other fieldsHigh correlation
loudness is highly overall correlated with acousticness and 3 other fieldsHigh correlation
popularity is highly overall correlated with acousticness and 2 other fieldsHigh correlation
valence is highly overall correlated with danceabilityHigh correlation
year is highly overall correlated with acousticness and 3 other fieldsHigh correlation
explicit is highly imbalanced (58.2%)Imbalance
id has unique valuesUnique
instrumentalness has 46580 (27.3%) zerosZeros
key has 21600 (12.7%) zerosZeros
popularity has 27892 (16.3%) zerosZeros

Reproduction

Analysis started2025-09-26 23:16:04.202847
Analysis finished2025-09-26 23:16:40.743768
Duration36.54 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

valence
Real number (ℝ)

High correlation 

Distinct1733
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.52858721
Minimum0
Maximum1
Zeros196
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2025-09-26T23:16:40.860269image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0905
Q10.317
median0.54
Q30.747
95-th percentile0.937
Maximum1
Range1
Interquartile range (IQR)0.43

Descriptive statistics

Standard deviation0.26317146
Coefficient of variation (CV)0.49787709
Kurtosis-1.0616275
Mean0.52858721
Median Absolute Deviation (MAD)0.215
Skewness-0.10711976
Sum90204.993
Variance0.069259219
MonotonicityNot monotonic
2025-09-26T23:16:40.997911image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.961718
 
0.4%
0.962593
 
0.3%
0.963515
 
0.3%
0.964462
 
0.3%
0.965400
 
0.2%
0.96388
 
0.2%
0.966349
 
0.2%
0.967316
 
0.2%
0.968269
 
0.2%
0.559248
 
0.1%
Other values (1723)166395
97.5%
ValueCountFrequency (%)
0196
0.1%
1 × 10-570
 
< 0.1%
6.41 × 10-51
 
< 0.1%
0.0005371
 
< 0.1%
0.0005621
 
< 0.1%
0.001261
 
< 0.1%
0.001661
 
< 0.1%
0.001731
 
< 0.1%
0.002131
 
< 0.1%
0.002281
 
< 0.1%
ValueCountFrequency (%)
14
< 0.1%
0.9981
 
< 0.1%
0.9962
 
< 0.1%
0.9951
 
< 0.1%
0.9943
 
< 0.1%
0.9934
< 0.1%
0.9914
< 0.1%
0.998
< 0.1%
0.9896
< 0.1%
0.9887
< 0.1%

year
Real number (ℝ)

High correlation 

Distinct100
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1976.7872
Minimum1921
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2025-09-26T23:16:41.139145image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1921
5-th percentile1933
Q11956
median1977
Q31999
95-th percentile2016
Maximum2020
Range99
Interquartile range (IQR)43

Descriptive statistics

Standard deviation25.917853
Coefficient of variation (CV)0.013111099
Kurtosis-1.0355084
Mean1976.7872
Median Absolute Deviation (MAD)22
Skewness-0.12943464
Sum3.3734467 × 108
Variance671.73508
MonotonicityNot monotonic
2025-09-26T23:16:41.280973image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20182103
 
1.2%
20202030
 
1.2%
20112017
 
1.2%
20102016
 
1.2%
20012005
 
1.2%
20142005
 
1.2%
19492000
 
1.2%
19552000
 
1.2%
19542000
 
1.2%
19782000
 
1.2%
Other values (90)150477
88.2%
ValueCountFrequency (%)
1921150
 
0.1%
192271
 
< 0.1%
1923185
 
0.1%
1924236
 
0.1%
1925278
 
0.2%
19261378
0.8%
1927615
 
0.4%
19281261
0.7%
1929952
0.6%
19301924
1.1%
ValueCountFrequency (%)
20202030
1.2%
20191949
1.1%
20182103
1.2%
20171992
1.2%
20161797
1.1%
20151974
1.2%
20142005
1.2%
20131976
1.2%
20121945
1.1%
20112017
1.2%

acousticness
Real number (ℝ)

High correlation 

Distinct4689
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.50211476
Minimum0
Maximum0.996
Zeros20
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2025-09-26T23:16:41.413971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.00147
Q10.102
median0.516
Q30.893
95-th percentile0.992
Maximum0.996
Range0.996
Interquartile range (IQR)0.791

Descriptive statistics

Standard deviation0.37603173
Coefficient of variation (CV)0.74889597
Kurtosis-1.6094281
Mean0.50211476
Median Absolute Deviation (MAD)0.395
Skewness-0.032582418
Sum85687.391
Variance0.14139986
MonotonicityNot monotonic
2025-09-26T23:16:41.561372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9953117
 
1.8%
0.9942323
 
1.4%
0.9931759
 
1.0%
0.9921513
 
0.9%
0.9911298
 
0.8%
0.991180
 
0.7%
0.9961058
 
0.6%
0.9891053
 
0.6%
0.988927
 
0.5%
0.987818
 
0.5%
Other values (4679)155607
91.2%
ValueCountFrequency (%)
020
< 0.1%
1 × 10-61
 
< 0.1%
1.01 × 10-63
 
< 0.1%
1.02 × 10-61
 
< 0.1%
1.03 × 10-61
 
< 0.1%
1.05 × 10-62
 
< 0.1%
1.07 × 10-61
 
< 0.1%
1.11 × 10-61
 
< 0.1%
1.15 × 10-61
 
< 0.1%
1.17 × 10-62
 
< 0.1%
ValueCountFrequency (%)
0.9961058
 
0.6%
0.9953117
1.8%
0.9942323
1.4%
0.9931759
1.0%
0.9921513
0.9%
0.9911298
0.8%
0.991180
 
0.7%
0.9891053
 
0.6%
0.988927
 
0.5%
0.987818
 
0.5%

artists
Text

Distinct34088
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
2025-09-26T23:16:41.925921image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length661
Median length345
Mean length23.369282
Min length5

Characters and Unicode

Total characters3988038
Distinct characters613
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20168 ?
Unique (%)11.8%

Sample

1st row['Sergei Rachmaninoff', 'James Levine', 'Berliner Philharmoniker']
2nd row['Dennis Day']
3rd row['KHP Kridhamardawa Karaton Ngayogyakarta Hadiningrat']
4th row['Frank Parker']
5th row['Phil Regan']
ValueCountFrequency (%)
the17347
 
3.5%
orchestra6068
 
1.2%
5771
 
1.2%
john2491
 
0.5%
francisco2357
 
0.5%
canaro2253
 
0.5%
los2034
 
0.4%
de1928
 
0.4%
his1784
 
0.4%
of1732
 
0.4%
Other values (25904)450708
91.1%
2025-09-26T23:16:42.432772image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
'450922
 
11.3%
323820
 
8.1%
e264139
 
6.6%
a252534
 
6.3%
r193143
 
4.8%
n188862
 
4.7%
i186004
 
4.7%
o179597
 
4.5%
]170658
 
4.3%
[170658
 
4.3%
Other values (603)1607701
40.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)3988038
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
'450922
 
11.3%
323820
 
8.1%
e264139
 
6.6%
a252534
 
6.3%
r193143
 
4.8%
n188862
 
4.7%
i186004
 
4.7%
o179597
 
4.5%
]170658
 
4.3%
[170658
 
4.3%
Other values (603)1607701
40.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3988038
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
'450922
 
11.3%
323820
 
8.1%
e264139
 
6.6%
a252534
 
6.3%
r193143
 
4.8%
n188862
 
4.7%
i186004
 
4.7%
o179597
 
4.5%
]170658
 
4.3%
[170658
 
4.3%
Other values (603)1607701
40.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3988038
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
'450922
 
11.3%
323820
 
8.1%
e264139
 
6.6%
a252534
 
6.3%
r193143
 
4.8%
n188862
 
4.7%
i186004
 
4.7%
o179597
 
4.5%
]170658
 
4.3%
[170658
 
4.3%
Other values (603)1607701
40.3%

danceability
Real number (ℝ)

High correlation 

Distinct1240
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.53739553
Minimum0
Maximum0.988
Zeros143
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2025-09-26T23:16:42.588880image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.23
Q10.415
median0.548
Q30.668
95-th percentile0.811
Maximum0.988
Range0.988
Interquartile range (IQR)0.253

Descriptive statistics

Standard deviation0.17613774
Coefficient of variation (CV)0.32776181
Kurtosis-0.44289742
Mean0.53739553
Median Absolute Deviation (MAD)0.126
Skewness-0.22347135
Sum91708.16
Variance0.031024502
MonotonicityNot monotonic
2025-09-26T23:16:42.727891image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.565430
 
0.3%
0.612408
 
0.2%
0.632402
 
0.2%
0.61401
 
0.2%
0.578399
 
0.2%
0.559398
 
0.2%
0.545397
 
0.2%
0.6395
 
0.2%
0.548394
 
0.2%
0.556393
 
0.2%
Other values (1230)166636
97.6%
ValueCountFrequency (%)
0143
0.1%
0.05511
 
< 0.1%
0.05592
 
< 0.1%
0.05692
 
< 0.1%
0.05741
 
< 0.1%
0.05831
 
< 0.1%
0.05871
 
< 0.1%
0.05891
 
< 0.1%
0.0591
 
< 0.1%
0.05911
 
< 0.1%
ValueCountFrequency (%)
0.9881
 
< 0.1%
0.9862
 
< 0.1%
0.9851
 
< 0.1%
0.9831
 
< 0.1%
0.984
< 0.1%
0.9793
< 0.1%
0.9783
< 0.1%
0.9775
< 0.1%
0.9761
 
< 0.1%
0.9756
< 0.1%

duration_ms
Real number (ℝ)

Distinct51755
Distinct (%)30.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean230948.31
Minimum5108
Maximum5403500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2025-09-26T23:16:42.869838image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5108
5-th percentile110987
Q1169827
median207467
Q3262400
95-th percentile411551.6
Maximum5403500
Range5398392
Interquartile range (IQR)92573

Descriptive statistics

Standard deviation126118.41
Coefficient of variation (CV)0.54608936
Kurtosis132.92182
Mean230948.31
Median Absolute Deviation (MAD)44467
Skewness7.3137407
Sum3.9412022 × 1010
Variance1.5905855 × 1010
MonotonicityNot monotonic
2025-09-26T23:16:43.242663image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19200056
 
< 0.1%
17000050
 
< 0.1%
18000049
 
< 0.1%
18600049
 
< 0.1%
16000048
 
< 0.1%
18400048
 
< 0.1%
24000046
 
< 0.1%
16800046
 
< 0.1%
19500045
 
< 0.1%
17500044
 
< 0.1%
Other values (51745)170172
99.7%
ValueCountFrequency (%)
51081
< 0.1%
59911
< 0.1%
63621
< 0.1%
64671
< 0.1%
88532
< 0.1%
96801
< 0.1%
103711
< 0.1%
119731
< 0.1%
134531
< 0.1%
136001
< 0.1%
ValueCountFrequency (%)
54035001
< 0.1%
42700341
< 0.1%
42694071
< 0.1%
41202582
< 0.1%
38163731
< 0.1%
36508001
< 0.1%
35699331
< 0.1%
35579551
< 0.1%
35568671
< 0.1%
35511521
< 0.1%

energy
Real number (ℝ)

High correlation 

Distinct2332
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.48238884
Minimum0
Maximum1
Zeros9
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2025-09-26T23:16:43.374325image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.076
Q10.255
median0.471
Q30.703
95-th percentile0.924
Maximum1
Range1
Interquartile range (IQR)0.448

Descriptive statistics

Standard deviation0.2676457
Coefficient of variation (CV)0.55483395
Kurtosis-1.1001327
Mean0.48238884
Median Absolute Deviation (MAD)0.224
Skewness0.11203494
Sum82321.102
Variance0.071634223
MonotonicityNot monotonic
2025-09-26T23:16:43.511317image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2247
 
0.1%
0.341244
 
0.1%
0.187242
 
0.1%
0.255241
 
0.1%
0.219239
 
0.1%
0.274238
 
0.1%
0.185238
 
0.1%
0.245237
 
0.1%
0.306237
 
0.1%
0.32237
 
0.1%
Other values (2322)168253
98.6%
ValueCountFrequency (%)
09
< 0.1%
1.99 × 10-51
 
< 0.1%
2 × 10-51
 
< 0.1%
2.01 × 10-57
< 0.1%
2.02 × 10-53
 
< 0.1%
2.03 × 10-510
< 0.1%
2.8 × 10-51
 
< 0.1%
3.22 × 10-51
 
< 0.1%
4.28 × 10-51
 
< 0.1%
4.98 × 10-51
 
< 0.1%
ValueCountFrequency (%)
121
 
< 0.1%
0.99928
 
< 0.1%
0.99838
< 0.1%
0.99751
< 0.1%
0.99664
< 0.1%
0.99581
< 0.1%
0.99476
< 0.1%
0.99373
< 0.1%
0.99259
< 0.1%
0.99188
0.1%

explicit
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
0
156220 
1
 
14433

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters170653
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0156220
91.5%
114433
 
8.5%

Length

2025-09-26T23:16:43.652066image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-26T23:16:43.732661image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0156220
91.5%
114433
 
8.5%

Most occurring characters

ValueCountFrequency (%)
0156220
91.5%
114433
 
8.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)170653
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0156220
91.5%
114433
 
8.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)170653
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0156220
91.5%
114433
 
8.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)170653
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0156220
91.5%
114433
 
8.5%

id
Text

Unique 

Distinct170653
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
2025-09-26T23:16:44.242019image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters3754366
Distinct characters62
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique170653 ?
Unique (%)100.0%

Sample

1st row4BJqT0PrAfrxzMOxytFOIz
2nd row7xPhfUan2yNtyFG0cUWkt8
3rd row1o6I8BglA6ylDMrIELygv1
4th row3ftBPsC5vPBKxYSee08FDH
5th row4d6HGyGT8e121BsdKmw9v6
ValueCountFrequency (%)
0lcxzabea84egudqpnun1i1
 
< 0.1%
7hmnjhfs0bkfzx4x8j0hkl1
 
< 0.1%
4bjqt0prafrxzmoxytfoiz1
 
< 0.1%
7xphfuan2yntyfg0cuwkt81
 
< 0.1%
1o6i8bgla6yldmrielygv11
 
< 0.1%
3ftbpsc5vpbkxysee08fdh1
 
< 0.1%
4d6hgygt8e121bsdkmw9v61
 
< 0.1%
4pyw9dvhgsture4j6hpngr1
 
< 0.1%
5unznelqos3w4frmrypk4t1
 
< 0.1%
02gdntoxexbfuvsgaxlpkd1
 
< 0.1%
Other values (170643)170643
> 99.9%
2025-09-26T23:16:44.700159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
082448
 
2.2%
181401
 
2.2%
280500
 
2.1%
379581
 
2.1%
478941
 
2.1%
578481
 
2.1%
677828
 
2.1%
773852
 
2.0%
L58509
 
1.6%
t58331
 
1.6%
Other values (52)3004494
80.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)3754366
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
082448
 
2.2%
181401
 
2.2%
280500
 
2.1%
379581
 
2.1%
478941
 
2.1%
578481
 
2.1%
677828
 
2.1%
773852
 
2.0%
L58509
 
1.6%
t58331
 
1.6%
Other values (52)3004494
80.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3754366
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
082448
 
2.2%
181401
 
2.2%
280500
 
2.1%
379581
 
2.1%
478941
 
2.1%
578481
 
2.1%
677828
 
2.1%
773852
 
2.0%
L58509
 
1.6%
t58331
 
1.6%
Other values (52)3004494
80.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3754366
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
082448
 
2.2%
181401
 
2.2%
280500
 
2.1%
379581
 
2.1%
478941
 
2.1%
578481
 
2.1%
677828
 
2.1%
773852
 
2.0%
L58509
 
1.6%
t58331
 
1.6%
Other values (52)3004494
80.0%

instrumentalness
Real number (ℝ)

Zeros 

Distinct5401
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.16700958
Minimum0
Maximum1
Zeros46580
Zeros (%)27.3%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2025-09-26T23:16:44.834541image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.000216
Q30.102
95-th percentile0.905
Maximum1
Range1
Interquartile range (IQR)0.102

Descriptive statistics

Standard deviation0.31347467
Coefficient of variation (CV)1.8769862
Kurtosis0.94219542
Mean0.16700958
Median Absolute Deviation (MAD)0.000216
Skewness1.631114
Sum28500.686
Variance0.098266371
MonotonicityNot monotonic
2025-09-26T23:16:44.972082image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
046580
 
27.3%
0.916201
 
0.1%
0.917197
 
0.1%
0.922191
 
0.1%
0.904191
 
0.1%
0.913188
 
0.1%
0.911186
 
0.1%
0.909185
 
0.1%
0.899185
 
0.1%
0.914184
 
0.1%
Other values (5391)122365
71.7%
ValueCountFrequency (%)
046580
27.3%
1 × 10-624
 
< 0.1%
1.01 × 10-667
 
< 0.1%
1.02 × 10-684
 
< 0.1%
1.03 × 10-669
 
< 0.1%
1.04 × 10-656
 
< 0.1%
1.05 × 10-658
 
< 0.1%
1.06 × 10-657
 
< 0.1%
1.07 × 10-667
 
< 0.1%
1.08 × 10-655
 
< 0.1%
ValueCountFrequency (%)
110
< 0.1%
0.99912
< 0.1%
0.9989
< 0.1%
0.9974
 
< 0.1%
0.9966
 
< 0.1%
0.9956
 
< 0.1%
0.9948
< 0.1%
0.99316
< 0.1%
0.99211
< 0.1%
0.9916
 
< 0.1%

key
Real number (ℝ)

Zeros 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.1998441
Minimum0
Maximum11
Zeros21600
Zeros (%)12.7%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2025-09-26T23:16:45.077662image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q38
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.5150939
Coefficient of variation (CV)0.67599986
Kurtosis-1.2710487
Mean5.1998441
Median Absolute Deviation (MAD)3
Skewness0.0058639886
Sum887369
Variance12.355885
MonotonicityNot monotonic
2025-09-26T23:16:45.163907image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
021600
12.7%
720803
12.2%
218823
11.0%
917571
10.3%
516430
9.6%
412933
7.6%
112886
7.6%
1012148
7.1%
810751
6.3%
1110670
6.3%
Other values (2)16038
9.4%
ValueCountFrequency (%)
021600
12.7%
112886
7.6%
218823
11.0%
37297
 
4.3%
412933
7.6%
516430
9.6%
68741
5.1%
720803
12.2%
810751
6.3%
917571
10.3%
ValueCountFrequency (%)
1110670
6.3%
1012148
7.1%
917571
10.3%
810751
6.3%
720803
12.2%
68741
5.1%
516430
9.6%
412933
7.6%
37297
 
4.3%
218823
11.0%

liveness
Real number (ℝ)

Distinct1740
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.20583866
Minimum0
Maximum1
Zeros12
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2025-09-26T23:16:45.280611image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0599
Q10.0988
median0.136
Q30.261
95-th percentile0.61
Maximum1
Range1
Interquartile range (IQR)0.1622

Descriptive statistics

Standard deviation0.17480466
Coefficient of variation (CV)0.84923146
Kurtosis5.0012722
Mean0.20583866
Median Absolute Deviation (MAD)0.0532
Skewness2.1543815
Sum35126.984
Variance0.03055667
MonotonicityNot monotonic
2025-09-26T23:16:45.413868image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1111849
 
1.1%
0.111663
 
1.0%
0.1091636
 
1.0%
0.1081624
 
1.0%
0.1071553
 
0.9%
0.1061508
 
0.9%
0.1121500
 
0.9%
0.1051481
 
0.9%
0.1031402
 
0.8%
0.1141379
 
0.8%
Other values (1730)155058
90.9%
ValueCountFrequency (%)
012
< 0.1%
0.009671
 
< 0.1%
0.01011
 
< 0.1%
0.01031
 
< 0.1%
0.01161
 
< 0.1%
0.0121
 
< 0.1%
0.01231
 
< 0.1%
0.01341
 
< 0.1%
0.01363
 
< 0.1%
0.01391
 
< 0.1%
ValueCountFrequency (%)
11
 
< 0.1%
0.9991
 
< 0.1%
0.9982
 
< 0.1%
0.9975
 
< 0.1%
0.9963
 
< 0.1%
0.99510
< 0.1%
0.9948
< 0.1%
0.9935
 
< 0.1%
0.99213
< 0.1%
0.99116
< 0.1%

loudness
Real number (ℝ)

High correlation 

Distinct25410
Distinct (%)14.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-11.46799
Minimum-60
Maximum3.855
Zeros0
Zeros (%)0.0%
Negative170622
Negative (%)> 99.9%
Memory size1.3 MiB
2025-09-26T23:16:45.549021image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-60
5-th percentile-22.02
Q1-14.615
median-10.58
Q3-7.183
95-th percentile-4.1306
Maximum3.855
Range63.855
Interquartile range (IQR)7.432

Descriptive statistics

Standard deviation5.6979429
Coefficient of variation (CV)-0.49685628
Kurtosis1.8468035
Mean-11.46799
Median Absolute Deviation (MAD)3.648
Skewness-1.0518411
Sum-1957046.9
Variance32.466553
MonotonicityNot monotonic
2025-09-26T23:16:45.700944image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-7.43627
 
< 0.1%
-6.94227
 
< 0.1%
-8.3226
 
< 0.1%
-6.66426
 
< 0.1%
-11.81525
 
< 0.1%
-7.56625
 
< 0.1%
-7.63225
 
< 0.1%
-8.99225
 
< 0.1%
-6.51924
 
< 0.1%
-11.45124
 
< 0.1%
Other values (25400)170399
99.9%
ValueCountFrequency (%)
-609
< 0.1%
-551
 
< 0.1%
-54.8371
 
< 0.1%
-54.3761
 
< 0.1%
-52.221
 
< 0.1%
-51.1231
 
< 0.1%
-51.081
 
< 0.1%
-50.1741
 
< 0.1%
-48.5871
 
< 0.1%
-48.2782
 
< 0.1%
ValueCountFrequency (%)
3.8551
< 0.1%
3.7441
< 0.1%
2.7991
< 0.1%
1.9631
< 0.1%
1.831
< 0.1%
1.4831
< 0.1%
1.3421
< 0.1%
1.2751
< 0.1%
1.0731
< 0.1%
1.0231
< 0.1%

mode
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
1
120635 
0
50018 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters170653
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1120635
70.7%
050018
29.3%

Length

2025-09-26T23:16:45.828221image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-26T23:16:45.895077image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1120635
70.7%
050018
29.3%

Most occurring characters

ValueCountFrequency (%)
1120635
70.7%
050018
29.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)170653
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1120635
70.7%
050018
29.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)170653
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1120635
70.7%
050018
29.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)170653
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1120635
70.7%
050018
29.3%

name
Text

Distinct133638
Distinct (%)78.3%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
2025-09-26T23:16:46.296127image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length203
Median length170
Mean length23.649329
Min length1

Characters and Unicode

Total characters4035829
Distinct characters1621
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique114535 ?
Unique (%)67.1%

Sample

1st rowPiano Concerto No. 3 in D Minor, Op. 30: III. Finale. Alla breve
2nd rowClancy Lowered the Boom
3rd rowGati Bali
4th rowDanny Boy
5th rowWhen Irish Eyes Are Smiling
ValueCountFrequency (%)
38799
 
5.1%
the22353
 
2.9%
in11337
 
1.5%
you9284
 
1.2%
i9264
 
1.2%
a8989
 
1.2%
of7726
 
1.0%
me6916
 
0.9%
no6735
 
0.9%
to6086
 
0.8%
Other values (60263)631900
83.2%
2025-09-26T23:16:47.084300image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
588736
 
14.6%
e348722
 
8.6%
a250147
 
6.2%
o234774
 
5.8%
n193853
 
4.8%
i193188
 
4.8%
r178505
 
4.4%
t170904
 
4.2%
s125579
 
3.1%
l120734
 
3.0%
Other values (1611)1630687
40.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)4035829
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
588736
 
14.6%
e348722
 
8.6%
a250147
 
6.2%
o234774
 
5.8%
n193853
 
4.8%
i193188
 
4.8%
r178505
 
4.4%
t170904
 
4.2%
s125579
 
3.1%
l120734
 
3.0%
Other values (1611)1630687
40.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4035829
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
588736
 
14.6%
e348722
 
8.6%
a250147
 
6.2%
o234774
 
5.8%
n193853
 
4.8%
i193188
 
4.8%
r178505
 
4.4%
t170904
 
4.2%
s125579
 
3.1%
l120734
 
3.0%
Other values (1611)1630687
40.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4035829
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
588736
 
14.6%
e348722
 
8.6%
a250147
 
6.2%
o234774
 
5.8%
n193853
 
4.8%
i193188
 
4.8%
r178505
 
4.4%
t170904
 
4.2%
s125579
 
3.1%
l120734
 
3.0%
Other values (1611)1630687
40.4%

popularity
Real number (ℝ)

High correlation  Zeros 

Distinct100
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.431794
Minimum0
Maximum100
Zeros27892
Zeros (%)16.3%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2025-09-26T23:16:47.276708image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q111
median33
Q348
95-th percentile66
Maximum100
Range100
Interquartile range (IQR)37

Descriptive statistics

Standard deviation21.826615
Coefficient of variation (CV)0.694412
Kurtosis-1.025591
Mean31.431794
Median Absolute Deviation (MAD)17
Skewness-0.003733875
Sum5363930
Variance476.40113
MonotonicityNot monotonic
2025-09-26T23:16:47.469221image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
027892
 
16.3%
433136
 
1.8%
443117
 
1.8%
413078
 
1.8%
423051
 
1.8%
403051
 
1.8%
392968
 
1.7%
362896
 
1.7%
462883
 
1.7%
12876
 
1.7%
Other values (90)115705
67.8%
ValueCountFrequency (%)
027892
16.3%
12876
 
1.7%
21733
 
1.0%
31467
 
0.9%
41114
 
0.7%
51018
 
0.6%
61017
 
0.6%
71116
 
0.7%
81128
 
0.7%
91213
 
0.7%
ValueCountFrequency (%)
1001
 
< 0.1%
991
 
< 0.1%
971
 
< 0.1%
964
 
< 0.1%
954
 
< 0.1%
944
 
< 0.1%
934
 
< 0.1%
9211
< 0.1%
919
< 0.1%
9011
< 0.1%
Distinct10968
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
Minimum1921-01-01 00:00:00
Maximum2020-11-24 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-09-26T23:16:47.665975image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T23:16:47.911136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

speechiness
Real number (ℝ)

Distinct1626
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.098393262
Minimum0
Maximum0.97
Zeros143
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2025-09-26T23:16:48.132599image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0281
Q10.0349
median0.045
Q30.0756
95-th percentile0.354
Maximum0.97
Range0.97
Interquartile range (IQR)0.0407

Descriptive statistics

Standard deviation0.16274007
Coefficient of variation (CV)1.6539758
Kurtosis17.000433
Mean0.098393262
Median Absolute Deviation (MAD)0.0131
Skewness4.0478485
Sum16791.105
Variance0.026484331
MonotonicityNot monotonic
2025-09-26T23:16:48.337387image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0347580
 
0.3%
0.0334571
 
0.3%
0.0328563
 
0.3%
0.0337561
 
0.3%
0.033559
 
0.3%
0.0349557
 
0.3%
0.0332557
 
0.3%
0.0362553
 
0.3%
0.0363553
 
0.3%
0.0344552
 
0.3%
Other values (1616)165047
96.7%
ValueCountFrequency (%)
0143
0.1%
0.02221
 
< 0.1%
0.02233
 
< 0.1%
0.02245
 
< 0.1%
0.02254
 
< 0.1%
0.02265
 
< 0.1%
0.02277
 
< 0.1%
0.02289
 
< 0.1%
0.02297
 
< 0.1%
0.0239
 
< 0.1%
ValueCountFrequency (%)
0.971
 
< 0.1%
0.9693
 
< 0.1%
0.9685
 
< 0.1%
0.96712
 
< 0.1%
0.96627
 
< 0.1%
0.96534
 
< 0.1%
0.96456
< 0.1%
0.96373
< 0.1%
0.96281
< 0.1%
0.961106
0.1%

tempo
Real number (ℝ)

Distinct84694
Distinct (%)49.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean116.86159
Minimum0
Maximum243.507
Zeros143
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2025-09-26T23:16:48.530954image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile74.1516
Q193.421
median114.729
Q3135.537
95-th percentile174.3738
Maximum243.507
Range243.507
Interquartile range (IQR)42.116

Descriptive statistics

Standard deviation30.708533
Coefficient of variation (CV)0.26277696
Kurtosis-0.077953213
Mean116.86159
Median Absolute Deviation (MAD)21.091
Skewness0.44974062
Sum19942781
Variance943.014
MonotonicityNot monotonic
2025-09-26T23:16:48.728729image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0143
 
0.1%
12020
 
< 0.1%
120.01220
 
< 0.1%
128.00519
 
< 0.1%
94.99719
 
< 0.1%
120.01118
 
< 0.1%
119.99417
 
< 0.1%
119.99316
 
< 0.1%
119.96916
 
< 0.1%
129.99516
 
< 0.1%
Other values (84684)170349
99.8%
ValueCountFrequency (%)
0143
0.1%
30.9461
 
< 0.1%
31.9881
 
< 0.1%
32.4661
 
< 0.1%
32.81
 
< 0.1%
32.9411
 
< 0.1%
33.3341
 
< 0.1%
33.3911
 
< 0.1%
33.9441
 
< 0.1%
34.4961
 
< 0.1%
ValueCountFrequency (%)
243.5071
< 0.1%
243.3721
< 0.1%
238.8951
< 0.1%
236.7991
< 0.1%
224.4371
< 0.1%
222.6051
< 0.1%
221.7411
< 0.1%
221.1121
< 0.1%
221.0582
< 0.1%
220.2291
< 0.1%

Interactions

2025-09-26T23:16:37.666066image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T23:16:15.240355image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T23:16:16.967267image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T23:16:18.518643image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T23:16:20.982488image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T23:16:23.030941image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T23:16:24.574624image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T23:16:26.379457image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T23:16:27.951722image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T23:16:29.478934image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T23:16:31.075496image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T23:16:33.232412image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T23:16:35.845249image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T23:16:37.788824image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T23:16:15.371580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T23:16:17.081280image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T23:16:18.656908image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T23:16:21.146290image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T23:16:23.148955image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T23:16:24.706454image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T23:16:26.494534image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T23:16:28.066779image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T23:16:29.598329image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T23:16:31.496683image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T23:16:33.400401image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T23:16:36.021587image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T23:16:37.906960image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T23:16:15.493803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T23:16:17.208879image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T23:16:18.842521image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T23:16:21.309471image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T23:16:23.264439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T23:16:24.824637image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T23:16:26.613839image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T23:16:28.183100image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T23:16:29.717374image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T23:16:31.614069image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T23:16:33.568230image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T23:16:36.203488image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T23:16:38.024766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T23:16:15.613701image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T23:16:17.324303image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T23:16:19.007977image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T23:16:21.482624image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-09-26T23:16:26.752092image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T23:16:28.301544image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T23:16:29.851471image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T23:16:31.738449image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-09-26T23:16:19.521286image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-09-26T23:16:25.289005image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-09-26T23:16:18.026284image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-09-26T23:16:22.909724image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-09-26T23:16:29.367262image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-09-26T23:16:33.047432image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T23:16:35.653768image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T23:16:37.548423image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-09-26T23:16:48.906336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
acousticnessdanceabilityduration_msenergyexplicitinstrumentalnesskeylivenessloudnessmodepopularityspeechinesstempovalenceyear
acousticness1.000-0.230-0.233-0.7590.2490.292-0.0180.017-0.6000.061-0.574-0.055-0.231-0.158-0.626
danceability-0.2301.000-0.0880.2170.283-0.2810.023-0.1160.2330.0620.1940.2390.0220.5400.195
duration_ms-0.233-0.0881.0000.1770.0340.106-0.003-0.0660.1250.0160.231-0.0940.013-0.1990.259
energy-0.7590.2170.1771.0000.155-0.2210.0270.0670.8140.0480.4870.1240.2630.3590.537
explicit0.2490.2830.0340.1551.0000.1470.0960.0670.1970.0790.2620.4680.0780.1120.340
instrumentalness0.292-0.2810.106-0.2210.1471.000-0.012-0.053-0.3390.038-0.297-0.101-0.078-0.167-0.288
key-0.0180.023-0.0030.0270.096-0.0121.000-0.0030.0190.2110.0080.0330.0020.0280.008
liveness0.017-0.116-0.0660.0670.067-0.053-0.0031.0000.0300.014-0.1140.112-0.005-0.010-0.102
loudness-0.6000.2330.1250.8140.197-0.3390.0190.0301.0000.0420.5050.0480.2090.2630.553
mode0.0610.0620.0160.0480.0790.0380.2110.0140.0421.0000.0570.0760.0160.0390.072
popularity-0.5740.1940.2310.4870.262-0.2970.008-0.1140.5050.0571.000-0.0900.1380.0060.863
speechiness-0.0550.239-0.0940.1240.468-0.1010.0330.1120.0480.076-0.0901.0000.0800.143-0.048
tempo-0.2310.0220.0130.2630.078-0.0780.002-0.0050.2090.0160.1380.0801.0000.1720.149
valence-0.1580.540-0.1990.3590.112-0.1670.028-0.0100.2630.0390.0060.1430.1721.000-0.030
year-0.6260.1950.2590.5370.340-0.2880.008-0.1020.5530.0720.863-0.0480.149-0.0301.000

Missing values

2025-09-26T23:16:39.319707image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-09-26T23:16:40.096551image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

valenceyearacousticnessartistsdanceabilityduration_msenergyexplicitidinstrumentalnesskeylivenessloudnessmodenamepopularityrelease_datespeechinesstempo
00.059419210.982['Sergei Rachmaninoff', 'James Levine', 'Berliner Philharmoniker']0.2798316670.21104BJqT0PrAfrxzMOxytFOIz0.878000100.665-20.0961Piano Concerto No. 3 in D Minor, Op. 30: III. Finale. Alla breve419210.036680.954
10.963019210.732['Dennis Day']0.8191805330.34107xPhfUan2yNtyFG0cUWkt80.00000070.160-12.4411Clancy Lowered the Boom519210.415060.936
20.039419210.961['KHP Kridhamardawa Karaton Ngayogyakarta Hadiningrat']0.3285000620.16601o6I8BglA6ylDMrIELygv10.91300030.101-14.8501Gati Bali519210.0339110.339
30.165019210.967['Frank Parker']0.2752100000.30903ftBPsC5vPBKxYSee08FDH0.00002850.381-9.3161Danny Boy319210.0354100.109
40.253019210.957['Phil Regan']0.4181666930.19304d6HGyGT8e121BsdKmw9v60.00000230.229-10.0961When Irish Eyes Are Smiling219210.0380101.665
50.196019210.579['KHP Kridhamardawa Karaton Ngayogyakarta Hadiningrat']0.6973950760.34604pyw9DVHGStUre4J6hPngr0.16800020.130-12.5061Gati Mardika619210.0700119.824
60.406019210.996['John McCormack']0.5181595070.20305uNZnElqOS3W4fRmRYPk4T0.00000000.115-10.5891The Wearing of the Green419210.061566.221
70.073119210.993['Sergei Rachmaninoff']0.3892187730.088002GDntOXexBFUvSgaXLPkd0.52700010.363-21.0910Morceaux de fantaisie, Op. 3: No. 2, Prélude in C-Sharp Minor. Lento219210.045692.867
80.721019210.996['Ignacio Corsini']0.4851615200.130005xDjWH9ub67nJJk82yfGf0.15100050.104-21.5080La Mañanita - Remasterizado01921-03-200.048364.678
90.771019210.982['Fortugé']0.6841965600.257008zfJvRLp7pjAb94MA9JmF0.00000080.504-16.4151Il Etait Syndiqué019210.3990109.378
valenceyearacousticnessartistsdanceabilityduration_msenergyexplicitidinstrumentalnesskeylivenessloudnessmodenamepopularityrelease_datespeechinesstempo
1706430.907020200.00952['DJ Scheme', 'Cordae', 'Ski Mask The Slump God', 'Take A Daytrip']0.9172283330.5690013C9D1X8NkG2Ak1RaGpRnnQ0.00000070.0774-10.4561Soda (feat. Take A Daytrip)662020-11-130.2790144.014
1706440.466020200.31000['Fleet Foxes']0.5622536130.686000308prODCCD0O660tIktbUi0.02250070.1250-8.4801Sunblind662020-09-220.0249103.054
1706450.169020200.99400['Ólafur Arnalds']0.2811905000.03330013MOQ6oQqkrZEDkZOHukCw0.95900060.0995-31.4601We Contain Multitudes (from home)702020-08-260.034890.250
1706460.522020200.20400['Gunna']0.5982306000.4720012f8y4CuG57UJEmkG3ujd0D0.00001500.1080-10.9911NASTY GIRL / ON CAMERA662020-05-220.2580120.080
1706470.083820200.97400['Najma Wallin']0.1751335000.0075906RuFOroO9VO0aMGEzirLHk0.92500070.1130-35.0721Med slutna ögon702020-02-210.045470.872
1706480.608020200.08460['Anuel AA', 'Daddy Yankee', 'KAROL G', 'Ozuna', 'J Balvin']0.7863017140.8080000KkIkfsLEJbrcIhYsCL7L50.00028970.0822-3.7021China722020-05-290.0881105.029
1706490.734020200.20600['Ashnikko']0.7171506540.7530000OStKKAuXlxA0fMH54Qs6E0.00000070.1010-6.0201Halloweenie III: Seven Days682020-10-230.0605137.936
1706500.637020200.10100['MAMAMOO']0.6342112800.8580004BZXVFYCb76Q0Klojq4piV0.00000940.2580-2.2260AYA762020-11-030.080991.688
1706510.195020200.00998['Eminem']0.6713371470.6230015SiZJoLXp3WOl3J4C8IK0d0.00000820.6430-7.1611Darkness702020-01-170.308075.055
1706520.642020200.13200['KEVVO', 'J Balvin']0.8561895070.7210017HmnJHfs0BkFzX4x8j0hkl0.00471070.1820-4.9281Billetes Azules (with J Balvin)742020-10-160.108094.991